Journal article
Segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling
NeuroImage (Orlando, Fla.), Vol.76(1), pp.11-23
08/01/2013
DOI: 10.1016/j.neuroimage.2013.02.069
PMID: 23523774
Abstract
We propose a novel method for the automatic segmentation of brain MRI images by using discriminative dictionary learning and sparse coding techniques. In the proposed method, dictionaries and classifiers are learned simultaneously from a set of brain atlases, which can then be used for the reconstruction and segmentation of an unseen target image. The proposed segmentation strategy is based on image reconstruction, which is in contrast to most existing atlas-based labeling approaches that rely on comparing image similarities between atlases and target images. In addition, we propose a Fixed Discriminative Dictionary Learning for Segmentation (F-DDLS) strategy, which can learn dictionaries offline and perform segmentations online, enabling a significant speed-up in the segmentation stage. The proposed method has been evaluated for the hippocampus segmentation of 80 healthy ICBM subjects and 202 ADNI images. The robustness of the proposed method, especially of our F-DDLS strategy, was validated by training and testing on different subject groups in the ADNI database. The influence of different parameters was studied and the performance of the proposed method was also compared with that of the nonlocal patch-based approach. The proposed method achieved a median Dice coefficient of 0.879 on 202 ADNI images and 0.890 on 80 ICBM subjects, which is competitive compared with state-of-the-art methods.
•Sparse representation technique is applied to segmentations of brain MR images.•Discriminative dictionary learning is used to achieve a fast implementation.•Validation is carried out on hippocampus of 80 ICBM subjects and 202 ADNI images.•Segmentation results demonstrate the accuracy of the proposed method.•The proposed method may provide a potential direction for human brain labeling.
Details
- Title: Subtitle
- Segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling
- Creators
- Tong Tong - Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queen's Gate, London, SW7 2AZ, UKRobin Wolz - Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queen's Gate, London, SW7 2AZ, UKPierrick Coupé - LaBRI, CNRS UMR 5800, 351 cours de la Libération, F-33405 Talence, FranceJoseph V Hajnal - Center for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas Hospital, London, SE1 7EH, UKDaniel Rueckert - Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queen's Gate, London, SW7 2AZ, UKAlzheimer's Disease Neuroimaging Initiative
- Contributors
- Laura L Boles-Ponto (Contributor) - University of Iowa, Radiology
- Resource Type
- Journal article
- Publication Details
- NeuroImage (Orlando, Fla.), Vol.76(1), pp.11-23
- DOI
- 10.1016/j.neuroimage.2013.02.069
- PMID
- 23523774
- NLM abbreviation
- Neuroimage
- ISSN
- 1053-8119
- eISSN
- 1095-9572
- Publisher
- Elsevier Inc
- Grant note
- DOI: 10.13039/501100000780, name: European Commission; DOI: 10.13039/501100004543, name: China Scholarship Council; DOI: 10.13039/100007333, name: Alzheimer's Disease Neuroimaging Initiative; name: NIH, award: U01 AG024904; DOI: 10.13039/100000049, name: National Institute on Aging; DOI: 10.13039/100000070, name: National Institute of Biomedical Imaging and Bioengineering (NIBIB); DOI: 10.13039/100004319, name: Pfizer Inc.; name: Wyeth Research; name: Bristol-Myers Squibb; DOI: 10.13039/100004312, name: Eli Lilly and Company; DOI: 10.13039/100004330, name: GlaxoSmithKline; DOI: 10.13039/100004334, name: Merck & Co. Inc.; name: AstraZeneca AB; DOI: 10.13039/100008272, name: Novartis Pharmaceuticals Corporation; DOI: 10.13039/100000957, name: Alzheimer's Association; name: Eisai Global Clinical Development; name: Elan Corporation plc; DOI: 10.13039/100005632, name: Forest Laboratories; name: Institute for the Study of Aging; DOI: 10.13039/100000038, name: U.S. Food and Drug Administration
- Language
- English
- Date published
- 08/01/2013
- Academic Unit
- Radiology; Pharmaceutical Sciences and Experimental Therapeutics
- Record Identifier
- 9984051593902771
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